Regular physical activity has been shown to be associated with reduced cancer risk (Lee et al, 2006 &McTiernan, 2008). Despite being aware of this, the majority of American adults do not currently meet physical activity guidelines (National Center for Health Statistics, 2007). Our research team has conducted numerous behavioral interventions aimed at encouraging physical activity adoption amongst previously sedentary adults (Marcus et al, 2007;Marcus, Lewis, Williams, Dunsiger et al, 2007;Pekmezi, Neighbors, Lee, Gans, Bock, Morrow, Marquez, Dunsiger &Marcus, 2009). Standard statistical methodology for analyzing data from these longitudinal trials may not suffice to describe patterns of physical activity adoption, maintenance and relapse. We propose a more sophisticated methodology aimed specifically at finding patterns of behavior change in six physical activity interventions. The ability to compare resulting sub-types of behavior change across studies (which themselves overlap in that they contain at least one treatment arm in common) will allow us to determine whether there are consistent patterns across studies and whether the distribution of these sub-types differs by treatment arm. In addition, we will identify potential predictors and moderators of pattern of behavior change by modifying our proposed computing routine to allow for other baseline variables to contribute to the distribution of sub-type. Finally, we will use these sub-types to identify key times in the treatment phase after which relapse to sedentary behavior is more likely. Each of these aims has specific implications for both design and analysis of future physical activity interventions. Using current methods we would be unable to carry out these same analyses and thus might risk missing key information about how participants change their cancer risk.